RTFN: A Robust Temporal Feature Network for Time Series Classification
This work addresses the challenge of obtaining sufficient representations for time series classification by considering both local and relational features, which is an incremental improvement for researchers working with time series data.
This paper proposes a Robust Temporal Feature Network (RTFN) for time series classification, which extracts both local features and the relationships among them. When embedded into supervised and unsupervised structures, RTFN-based methods achieve excellent performance on numerous UCR2018 and UEA2018 datasets.
Time series data usually contains local and global patterns. Most of the existing feature networks pay more attention to local features rather than the relationships among them. The latter is, however, also important yet more difficult to explore. To obtain sufficient representations by a feature network is still challenging. To this end, we propose a novel robust temporal feature network (RTFN) for feature extraction in time series classification, containing a temporal feature network (TFN) and an LSTM-based attention network (LSTMaN). TFN is a residual structure with multiple convolutional layers. It functions as a local-feature extraction network to mine sufficient local features from data. LSTMaN is composed of two identical layers, where attention and long short-term memory (LSTM) networks are hybridized. This network acts as a relation extraction network to discover the intrinsic relationships among the extracted features at different positions in sequential data. In experiments, we embed RTFN into a supervised structure as a feature extractor and into an unsupervised structure as an encoder, respectively. The results show that the RTFN-based structures achieve excellent supervised and unsupervised performance on a large number of UCR2018 and UEA2018 datasets.